docstr: Initialize a new agent.
docstr_tok: ["Initialize","a","new","agent","."]
code: def __init__(self, action_space):
      """Initialize a new agent."""
      BaseAgent.__init__(self, action_space=action_space)
  
      self.action_space = action_space
  
      self.actions = []
      actions_vec = np.load("./saved_files/top1000_actions.npz")[
          "actions"
      ]
      for i in range(actions_vec.shape[0]):
          act = action_space.from_vect(actions_vec[i])
          self.actions.append(act)
  
      self.actions = self.actions[:1000]
      self.act_num = len(self.actions)
      self.sub_ids = np.load("./saved_files/sub_id_info.npz")["sub_ids"]
      self.do_nothing_action = action_space({})
      self.origin_ids = range(len(self.actions))
  
      offset = action_space.n_line
      self.action_to_sub_topo = {}
      for sub_id, sub_elem_num in enumerate(action_space.sub_info):
          self.action_to_sub_topo[sub_id] = (
              offset,
              offset + sub_elem_num,
          )
          offset += sub_elem_num
      self.step = 0
      self.powernet_model = PowerNetModel()
      self.to_print_data = []
  
      self.last_disconnect_step = -100
      self.last_diconnect_line = None
      self.simulation_times = 0
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/rl_agent.py
imports: import os
  import numpy
  from copy import deepcopy
  from utils import process
  from powernet_model import PowerNetModel
  from grid2op.Agent import BaseAgent
  import torch
label_desc: Use image node_id 1 to create a new RLAgent object from inherited base classes: BaseAgent with example: obj = RLAgent(action_space)